A downward translation in the polynomial hierarchy

1997 ◽  
pp. 319-328 ◽  
Author(s):  
Edith Hemaspaandra ◽  
Lane A. Hemaspaandra ◽  
Harald Hempel
Keyword(s):  
Polynomial Hierarchy

Computing Solutions Uniquely Collapses the Polynomial Hierarchy

1996 ◽  
Vol 25 (4) ◽  
pp. 697-708 ◽  
Author(s):  
Lane A. Hemaspaandra ◽  
Ashish V. Naik ◽  
Mitsunori Ogihara ◽  
Alan L. Selman
Keyword(s):  
Polynomial Hierarchy

Logical Characterizations of Bounded Query Classes I: Logspace Oracle Machines

1993 ◽  
Vol 18 (1) ◽  
pp. 65-92
Author(s):  
Iain A. Stewart
Keyword(s):  
Truth Table ◽  
Complexity Class ◽  
Polynomial Hierarchy ◽  
Complexity Classes ◽  
Turing Machines ◽  
Complete Problems

We consider three sub-logics of the logic (±HP)*[FOs] and show that these sub-logics capture the complexity classes obtained by considering logspace deterministic oracle Turing machines with oracles in NP where the number of oracle calls is unrestricted and constant, respectively; that is, the classes LNP and LNP[O(1)]. We conclude that if certain logics are of the same expressibility then the Polynomial Hierarchy collapses. We also exhibit some new complete problems for the complexity class LNP via projection translations (the first to be discovered: projection translations are extremely weak logical reductions between problems) and characterize the complexity class LNP[O(1)] as the closure of NP under a new, extremely strict truth-table reduction (which we introduce in this paper).

scholarly journals Further extensions of Clifford circuits and their classical simulation complexities

2017 ◽  
Vol 17 (3&4) ◽  
pp. 262-282
Author(s):  
Dax E. Koh
Keyword(s):  
Quantum Circuit ◽  
Complete Classification ◽  
Polynomial Hierarchy ◽  
New Combinations ◽  
Computational Power ◽  
Classical Simulation

Extended Clifford circuits straddle the boundary between classical and quantum computational power. Whether such circuits are efficiently classically simulable seems to depend delicately on the ingredients of the circuits. While some combinations of ingredients lead to efficiently classically simulable circuits, other combinations, which might just be slightly different, lead to circuits which are likely not. We extend the results of Jozsa and Van den Nest [Quant. Info. Comput. 14, 633 (2014)] by studying two further extensions of Clifford circuits. First, we consider how the classical simulation complexity changes when we allow for more general measurements. Second, we investigate different notions of what it means to ‘classically simulate’ a quantum circuit. These further extensions give us 24 new combinations of ingredients compared to Jozsa and Van den Nest, and we give a complete classification of their classical simulation complexities. Our results provide more examples where seemingly modest changes to the ingredients of Clifford circuits lead to “large” changes in the classical simulation complexities of the circuits, and also include new examples of extended Clifford circuits that exhibit “quantum supremacy”, in the sense that it is not possible to efficiently classically sample from the output distributions of such circuits, unless the polynomial hierarchy collapses.

scholarly journals Placing quantified variants of 3-SAT and Not-All-Equal 3-SAT in the polynomial hierarchy

2020 ◽  
Vol 822 ◽  
pp. 72-91
Author(s):  
Janosch Döcker ◽  
Britta Dorn ◽  
Simone Linz ◽  
Charles Semple
Keyword(s):  
Polynomial Hierarchy

scholarly journals Beyond NP: Quantifying over Answer Sets

2019 ◽  
Vol 19 (5-6) ◽  
pp. 705-721
Author(s):  
GIOVANNI AMENDOLA ◽  
FRANCESCO RICCA ◽  
MIROSLAW TRUSZCZYNSKI
Keyword(s):  
Answer Set Programming ◽  
The Other ◽  
Polynomial Hierarchy ◽  
Programming Paradigm ◽  
Quantified Boolean Formulas ◽  
Boolean Formulas ◽  
The One ◽  
Image Position ◽  
Answer Sets ◽  
Computational Properties

AbstractAnswer Set Programming (ASP) is a logic programming paradigm featuring a purely declarative language with comparatively high modeling capabilities. Indeed, ASP can model problems in NP in a compact and elegant way. However, modeling problems beyond NP with ASP is known to be complicated, on the one hand, and limited to problems in $\[\Sigma _2^P\]$ on the other. Inspired by the way Quantified Boolean Formulas extend SAT formulas to model problems beyond NP, we propose an extension of ASP that introduces quantifiers over stable models of programs. We name the new language ASP with Quantifiers (ASP(Q)). In the paper we identify computational properties of ASP(Q); we highlight its modeling capabilities by reporting natural encodings of several complex problems with applications in artificial intelligence and number theory; and we compare ASP(Q) with related languages. Arguably, ASP(Q) allows one to model problems in the Polynomial Hierarchy in a direct way, providing an elegant expansion of ASP beyond the class NP.

scholarly journals Learning Complexity vs Communication Complexity

2009 ◽  
Vol 18 (1-2) ◽  
pp. 227-245 ◽  
Author(s):  
NATI LINIAL ◽  
ADI SHRAIBMAN
Keyword(s):  
Communication Complexity ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Polynomial Hierarchy ◽  
Open Problems ◽  
Multiplicative Constant ◽  
Large Margin ◽  
Vector Machines ◽  
Large Margin Classifiers ◽  
Class Of Functions

This paper has two main focal points. We first consider an important class of machine learning algorithms: large margin classifiers, such as Support Vector Machines. The notion of margin complexity quantifies the extent to which a given class of functions can be learned by large margin classifiers. We prove that up to a small multiplicative constant, margin complexity is equal to the inverse of discrepancy. This establishes a strong tie between seemingly very different notions from two distinct areas.In the same way that matrix rigidity is related to rank, we introduce the notion of rigidity of margin complexity. We prove that sign matrices with small margin complexity rigidity are very rare. This leads to the question of proving lower bounds on the rigidity of margin complexity. Quite surprisingly, this question turns out to be closely related to basic open problems in communication complexity, e.g., whether PSPACE can be separated from the polynomial hierarchy in communication complexity.Communication is a key ingredient in many types of learning. This explains the relations between the field of learning theory and that of communication complexity [6, l0, 16, 26]. The results of this paper constitute another link in this rich web of relations. These new results have already been applied toward the solution of several open problems in communication complexity [18, 20, 29].

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